


"""simple docstring"""



import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

lowercase_							=							[
    # (stable-diffusion, HF Diffusers)
    ('time_embed.0.weight', 'time_embedding.linear_1.weight'),
    ('time_embed.0.bias', 'time_embedding.linear_1.bias'),
    ('time_embed.2.weight', 'time_embedding.linear_2.weight'),
    ('time_embed.2.bias', 'time_embedding.linear_2.bias'),
    ('input_blocks.0.0.weight', 'conv_in.weight'),
    ('input_blocks.0.0.bias', 'conv_in.bias'),
    ('out.0.weight', 'conv_norm_out.weight'),
    ('out.0.bias', 'conv_norm_out.bias'),
    ('out.2.weight', 'conv_out.weight'),
    ('out.2.bias', 'conv_out.bias'),
]

lowercase_							=							[
    # (stable-diffusion, HF Diffusers)
    ('in_layers.0', 'norm1'),
    ('in_layers.2', 'conv1'),
    ('out_layers.0', 'norm2'),
    ('out_layers.3', 'conv2'),
    ('emb_layers.1', 'time_emb_proj'),
    ('skip_connection', 'conv_shortcut'),
]

lowercase_							=							[]
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
		# loop over downblocks/upblocks

		for j in range(2):
				# loop over resnets/attentions for downblocks
				lowercase_							=							f"""down_blocks.{i}.resnets.{j}."""
				lowercase_							=							f"""input_blocks.{3*i + j + 1}.0."""
				unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

				if i < 3:
						# no attention layers in down_blocks.3
						lowercase_							=							f"""down_blocks.{i}.attentions.{j}."""
						lowercase_							=							f"""input_blocks.{3*i + j + 1}.1."""
						unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

		for j in range(3):
				# loop over resnets/attentions for upblocks
				lowercase_							=							f"""up_blocks.{i}.resnets.{j}."""
				lowercase_							=							f"""output_blocks.{3*i + j}.0."""
				unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

				if i > 0:
						# no attention layers in up_blocks.0
						lowercase_							=							f"""up_blocks.{i}.attentions.{j}."""
						lowercase_							=							f"""output_blocks.{3*i + j}.1."""
						unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

		if i < 3:
				# no downsample in down_blocks.3
				lowercase_							=							f"""down_blocks.{i}.downsamplers.0.conv."""
				lowercase_							=							f"""input_blocks.{3*(i+1)}.0.op."""
				unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

				# no upsample in up_blocks.3
				lowercase_							=							f"""up_blocks.{i}.upsamplers.0."""
				lowercase_							=							f"""output_blocks.{3*i + 2}.{1 if i == 0 else 2}."""
				unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

lowercase_							=							'mid_block.attentions.0.'
lowercase_							=							'middle_block.1.'
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
		lowercase_							=							f"""mid_block.resnets.{j}."""
		lowercase_							=							f"""middle_block.{2*j}."""
		unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
def      UpperCAmelCase     (       _lowercase :   List[Any]	)						-> Optional[Any]:


	"""simple docstring"""




	lowerCAmelCase_      							=			{k: k for k in unet_state_dict.keys()}
	for sd_name, hf_name in unet_conversion_map:
		lowerCAmelCase_      							=			sd_name
	for k, v in mapping.items():
		if "resnets" in k:
			for sd_part, hf_part in unet_conversion_map_resnet:
				lowerCAmelCase_      							=			v.replace(_lowercase			,   _lowercase	)
			lowerCAmelCase_      							=			v
	for k, v in mapping.items():
		for sd_part, hf_part in unet_conversion_map_layer:
			lowerCAmelCase_      							=			v.replace(_lowercase			,   _lowercase	)
		lowerCAmelCase_      							=			v
	lowerCAmelCase_      							=			{v: unet_state_dict[k] for k, v in mapping.items()}
	return new_state_dict


# ================#
# VAE Conversion #
# ================#

lowercase_							=							[
    # (stable-diffusion, HF Diffusers)
    ('nin_shortcut', 'conv_shortcut'),
    ('norm_out', 'conv_norm_out'),
    ('mid.attn_1.', 'mid_block.attentions.0.'),
]

for i in range(4):
		# down_blocks have two resnets
		for j in range(2):
				lowercase_							=							f"""encoder.down_blocks.{i}.resnets.{j}."""
				lowercase_							=							f"""encoder.down.{i}.block.{j}."""
				vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

		if i < 3:
				lowercase_							=							f"""down_blocks.{i}.downsamplers.0."""
				lowercase_							=							f"""down.{i}.downsample."""
				vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

				lowercase_							=							f"""up_blocks.{i}.upsamplers.0."""
				lowercase_							=							f"""up.{3-i}.upsample."""
				vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

		# up_blocks have three resnets
		# also, up blocks in hf are numbered in reverse from sd
		for j in range(3):
				lowercase_							=							f"""decoder.up_blocks.{i}.resnets.{j}."""
				lowercase_							=							f"""decoder.up.{3-i}.block.{j}."""
				vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
		lowercase_							=							f"""mid_block.resnets.{i}."""
		lowercase_							=							f"""mid.block_{i+1}."""
		vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


lowercase_							=							[
    # (stable-diffusion, HF Diffusers)
    ('norm.', 'group_norm.'),
    ('q.', 'query.'),
    ('k.', 'key.'),
    ('v.', 'value.'),
    ('proj_out.', 'proj_attn.'),
]
def      UpperCAmelCase     (       _lowercase :   Union[str, Any]	)						-> Tuple:


	"""simple docstring"""




	return w.reshape(*w.shape			,   1			,   1	)
def      UpperCAmelCase     (       _lowercase :   Optional[Any]	)						-> List[str]:


	"""simple docstring"""




	lowerCAmelCase_      							=			{k: k for k in vae_state_dict.keys()}
	for k, v in mapping.items():
		for sd_part, hf_part in vae_conversion_map:
			lowerCAmelCase_      							=			v.replace(_lowercase			,   _lowercase	)
		lowerCAmelCase_      							=			v
	for k, v in mapping.items():
		if "attentions" in k:
			for sd_part, hf_part in vae_conversion_map_attn:
				lowerCAmelCase_      							=			v.replace(_lowercase			,   _lowercase	)
			lowerCAmelCase_      							=			v
	lowerCAmelCase_      							=			{v: vae_state_dict[k] for k, v in mapping.items()}
	lowerCAmelCase_      							=			['''q''', '''k''', '''v''', '''proj_out''']
	for k, v in new_state_dict.items():
		for weight_name in weights_to_convert:
			if F"""mid.attn_1.{weight_name}.weight""" in k:
				print(F"""Reshaping {k} for SD format"""	)
				lowerCAmelCase_      							=			reshape_weight_for_sd(_lowercase	)
	return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


lowercase_							=							[
    # (stable-diffusion, HF Diffusers)
    ('resblocks.', 'text_model.encoder.layers.'),
    ('ln_1', 'layer_norm1'),
    ('ln_2', 'layer_norm2'),
    ('.c_fc.', '.fc1.'),
    ('.c_proj.', '.fc2.'),
    ('.attn', '.self_attn'),
    ('ln_final.', 'transformer.text_model.final_layer_norm.'),
    ('token_embedding.weight', 'transformer.text_model.embeddings.token_embedding.weight'),
    ('positional_embedding', 'transformer.text_model.embeddings.position_embedding.weight'),
]
lowercase_							=							{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
lowercase_							=							re.compile('|'.join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
lowercase_							=							{'q': 0, 'k': 1, 'v': 2}
def      UpperCAmelCase     (       _lowercase :   Optional[Any]	)						-> Tuple:


	"""simple docstring"""




	lowerCAmelCase_      							=			{}
	lowerCAmelCase_      							=			{}
	lowerCAmelCase_      							=			{}
	for k, v in text_enc_dict.items():
		if (
		    k.endswith('''.self_attn.q_proj.weight'''	)
		    or k.endswith('''.self_attn.k_proj.weight'''	)
		    or k.endswith('''.self_attn.v_proj.weight'''	)
		):
			lowerCAmelCase_      							=			k[: -len('''.q_proj.weight'''	)]
			lowerCAmelCase_      							=			k[-len('''q_proj.weight'''	)]
			if k_pre not in capture_qkv_weight:
				lowerCAmelCase_      							=			[None, None, None]
			lowerCAmelCase_      							=			v
			continue

		if (
		    k.endswith('''.self_attn.q_proj.bias'''	)
		    or k.endswith('''.self_attn.k_proj.bias'''	)
		    or k.endswith('''.self_attn.v_proj.bias'''	)
		):
			lowerCAmelCase_      							=			k[: -len('''.q_proj.bias'''	)]
			lowerCAmelCase_      							=			k[-len('''q_proj.bias'''	)]
			if k_pre not in capture_qkv_bias:
				lowerCAmelCase_      							=			[None, None, None]
			lowerCAmelCase_      							=			v
			continue

		lowerCAmelCase_      							=			textenc_pattern.sub(lambda _lowercase	: protected[re.escape(m.group(0	)	)]			,   _lowercase	)
		lowerCAmelCase_      							=			v

	for k_pre, tensors in capture_qkv_weight.items():
		if None in tensors:
			raise Exception('''CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'''	)
		lowerCAmelCase_      							=			textenc_pattern.sub(lambda _lowercase	: protected[re.escape(m.group(0	)	)]			,   _lowercase	)
		lowerCAmelCase_      							=			torch.cat(_lowercase	)

	for k_pre, tensors in capture_qkv_bias.items():
		if None in tensors:
			raise Exception('''CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'''	)
		lowerCAmelCase_      							=			textenc_pattern.sub(lambda _lowercase	: protected[re.escape(m.group(0	)	)]			,   _lowercase	)
		lowerCAmelCase_      							=			torch.cat(_lowercase	)

	return new_state_dict
def      UpperCAmelCase     (       _lowercase :   Any	)						-> str:


	"""simple docstring"""




	return text_enc_dict


if __name__ == "__main__":
		lowercase_							=							argparse.ArgumentParser()

		parser.add_argument('--model_path', default=None, type=str, required=True, help='Path to the model to convert.')
		parser.add_argument('--checkpoint_path', default=None, type=str, required=True, help='Path to the output model.')
		parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
		parser.add_argument(
		    '--use_safetensors', action='store_true', help='Save weights use safetensors, default is ckpt.'
		)

		lowercase_							=							parser.parse_args()

		assert args.model_path is not None, "Must provide a model path!"

		assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

		# Path for safetensors
		lowercase_							=							osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.safetensors')
		lowercase_							=							osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.safetensors')
		lowercase_							=							osp.join(args.model_path, 'text_encoder', 'model.safetensors')

		# Load models from safetensors if it exists, if it doesn't pytorch
		if osp.exists(unet_path):
				lowercase_							=							load_file(unet_path, device='cpu')
		else:
				lowercase_							=							osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.bin')
				lowercase_							=							torch.load(unet_path, map_location='cpu')

		if osp.exists(vae_path):
				lowercase_							=							load_file(vae_path, device='cpu')
		else:
				lowercase_							=							osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.bin')
				lowercase_							=							torch.load(vae_path, map_location='cpu')

		if osp.exists(text_enc_path):
				lowercase_							=							load_file(text_enc_path, device='cpu')
		else:
				lowercase_							=							osp.join(args.model_path, 'text_encoder', 'pytorch_model.bin')
				lowercase_							=							torch.load(text_enc_path, map_location='cpu')

		# Convert the UNet model
		lowercase_							=							convert_unet_state_dict(unet_state_dict)
		lowercase_							=							{'model.diffusion_model.' + k: v for k, v in unet_state_dict.items()}

		# Convert the VAE model
		lowercase_							=							convert_vae_state_dict(vae_state_dict)
		lowercase_							=							{'first_stage_model.' + k: v for k, v in vae_state_dict.items()}

		# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
		lowercase_							=							'text_model.encoder.layers.22.layer_norm2.bias' in text_enc_dict

		if is_vaa_model:
				# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
				lowercase_							=							{'transformer.' + k: v for k, v in text_enc_dict.items()}
				lowercase_							=							convert_text_enc_state_dict_vaa(text_enc_dict)
				lowercase_							=							{'cond_stage_model.model.' + k: v for k, v in text_enc_dict.items()}
		else:
				lowercase_							=							convert_text_enc_state_dict(text_enc_dict)
				lowercase_							=							{'cond_stage_model.transformer.' + k: v for k, v in text_enc_dict.items()}

		# Put together new checkpoint
		lowercase_							=							{**unet_state_dict, **vae_state_dict, **text_enc_dict}
		if args.half:
				lowercase_							=							{k: v.half() for k, v in state_dict.items()}

		if args.use_safetensors:
				save_file(state_dict, args.checkpoint_path)
		else:
				lowercase_							=							{'state_dict': state_dict}
				torch.save(state_dict, args.checkpoint_path)



"""simple docstring"""



def      UpperCAmelCase     (       _lowercase :   int = 1_0_0_0	)						-> int:


	"""simple docstring"""




	lowerCAmelCase_							,	lowerCAmelCase_      							=			1, 1
	lowerCAmelCase_      							=			[]
	for i in range(1			,   n + 1	):
		lowerCAmelCase_      							=			prev_numerator + 2 * prev_denominator
		lowerCAmelCase_      							=			prev_numerator + prev_denominator
		if len(str(_lowercase	)	) > len(str(_lowercase	)	):
			result.append(_lowercase	)
		lowerCAmelCase_      							=			numerator
		lowerCAmelCase_      							=			denominator

	return len(_lowercase	)


if __name__ == "__main__":
		print(f"""{solution() = }""")